The need and demand for an accurate, non-invasive method for determining attributes of tissue, other biological samples or analyte concentrations in tissue or blood are well documented. For example, accurate non-invasive measurement of blood glucose levels in patients, particularly diabetics, would greatly improve treatment. Barnes et al. (U.S. Pat. No. 5,379,764) disclose the necessity for diabetics to frequently monitor glucose levels in their blood. It is further recognized that the more frequent the analysis, the less likely there will be large swings in glucose levels. These large swings are associated with the symptoms and complications of the disease, whose long-term effects can include heart disease, arteriosclerosis, blindness, stroke, hypertension, kidney failure, and premature death. As described below, several systems have been proposed for the non-invasive measurement of glucose in blood. However, despite these efforts, a lancet cut into the finger is still necessary for all presently commercially available forms of home glucose monitoring. This is believed so compromising to the diabetic patient that the most effective use of any form of diabetic management is rarely achieved.
The various proposed non-invasive methods for determining blood glucose level generally utilize quantitative infrared spectroscopy as a theoretical basis for analysis. In general, these methods involve probing glucose containing tissue using infrared radiation in absorption or attenuated total reflectance mode. Infrared spectroscopy measures the electromagnetic radiation (0.7-25 .mu.m) a substance absorbs all various wavelengths. Molecules do not maintain fixed positions with respect to each other, but vibrate back and forth about an average distance. Absorption of light at the appropriate energy causes the molecules to become excited to a higher vibration level. The excitation of the molecules to an excited state occurs only at certain discrete energy levels, which are characteristic for that particular molecule. The most primary vibrational states occur in the mid-infrared frequency region (i.e., 2.5-25 .mu.m). However, non-invasive analyte determination in blood in this region is problematic, if not impossible, due to the absorption of the light by water. The problem is overcome through the use of shorter wavelengths of light which are not as attenuated by water. Overtones of the primary vibrational states exist at shorter wavelengths and enable quantitative determinations at these wavelengths.
It is known that glucose absorbs at multiple frequencies in both the mid- and near-infrared range. There are, however, other infrared active analytes in the tissue and blood that also absorb at similar frequencies. Due to the overlapping nature of these absorption bands, no single or specific frequency can be used for reliable non-invasive glucose measurement. Analysis of spectral data for glucose measurement thus requires evaluation of many spectral intensities over a wide spectral range to achieve the sensitivity, precision, accuracy, and reliability necessary for quantitative determination. In addition to overlapping absorption bands, measurement of glucose is further complicated by the fact that glucose is a minor component by weight in blood and tissue, and that the resulting spectral data may exhibit a non-linear response due to both the properties of the substance being examined and/or inherent non-linearities in optical instrumentation.
A further common element to non-invasive glucose measuring techniques is the necessity for an optical interface between the body portion at the point of measurement and the sensor element of the analytical instrument. Generally, the sensor element must include an input element or means for irradiating the sample point with the infrared energy. The sensor element must further include an output element or means for measuring transmitted or reflected energy at various wavelengths resulting from irradiation through the input element. The optical interface also introduces variability into the non-invasive measurement.
Robinson et al. (U.S. Pat. No. 4,975,581) disclose a method and apparatus for measuring a characteristic of unknown value in a biological sample using infrared spectroscopy in conjunction with a multivariate model that is empirically derived from a set of spectra of biological samples of known characteristic values. The above-mentioned characteristic is generally the concentration of an analyte, such as glucose, but also may be any chemical or physical property of the sample. The method of Robinson et al. involves a two-step process that includes both calibration and prediction steps. In the calibration step, the infrared light is coupled to calibration samples of known characteristic values so that there is differential attenuation of at least several wavelengths of the infrared radiation as a function of the various components and analytes comprising the sample with known characteristic value. The infrared light is coupled to the sample by passing the light through the sample or by reflecting the light from the sample. Absorption of the infrared light by the sample causes intensity variations of the light that are a function of the wavelength of the light. The resulting intensity variations at the at least several wavelengths are measured for the set of calibration samples of known characteristic values. Original or transformed intensity variations are then empirically related to the known characteristic of the calibration samples using a multivariate algorithm to obtain a multivariate calibration model. In the prediction step, the infrared light is coupled to a sample of unknown characteristic value, and the calibration model is applied to the original or transformed intensity variations of the appropriate wavelengths of light measured from this unknown sample. The result of the prediction step is the estimated value of the characteristic of the unknown sample. The disclosure of Robinson et al. is incorporated herein by reference.
Barnes et al. (U.S. Pat. No. 5,379,764) disclose a spectrographic method for analyzing glucose concentration wherein near infrared radiation is projected on a portion of the body, the radiation including a plurality of wavelengths, followed by sensing the resulting radiation emitted from the portion of the body as affected by the absorption of the body. The method disclosed includes pretreating the resulting data to minimize influences of offset and drift to obtain an expression of the magnitude of the sensed radiation as modified.
Dahne et al. (U.S. Pat. No. 4,655,225) disclose the employment of near infrared spectroscopy for non-invasively transmitting optical energy in the near infrared spectrum through a finger or earlobe of a subject. Also discussed is the use of near infrared energy diffusely reflected from deep within the tissues. Responses are derived at two different wavelengths to quantify glucose in the subject. One of the wavelengths is used to determine background absorption, while the other wavelength is used to determine glucose absorption.
Caro (U.S. Pat. No. 5,348,003) discloses the use of temporally modulated electromagnetic energy at multiple wavelengths as the irradiating light energy. The derived wavelength dependence of the optical absorption per unit path length is compared with a calibration model to derive concentrations of an analyte in the medium.
Wu et al. (U.S. Pat. No. 5,452,723) disclose a method of spectrographic analysis of a tissue sample which includes measuring the diffuse reflectance spectrum, as well as a second selected spectrum, such as fluorescence, and adjusting the spectrum with the reflectance spectrum. Wu et al. assert that this procedure reduces the sample-to-sample variability.
The intended benefit of using models such as those disclosed above, including multivariate analysis as disclosed by Robinson, is that direct measurements that are important but costly, time consuming, or difficult to obtain, may be replaced by other indirect measurements that are cheaper and easier to get. However, none of the prior art modeling methods, as disclosed, has proven to be sufficiently robust or accurate to be used as a surrogate or replacement for direct measurement of an analyte such as glucose.
Of particular importance to the present invention is the use of multivariate analysis. Measurement by multivariate analysis involves a two-step process. In the first step, calibration, a model is constructed utilizing a dataset obtained by concurrently making indirect measurements and direct measurements (e.g., by invasively drawing or taking and analyzing a biological sample such as blood for glucose levels) in a number of situations spanning a variety of physiological and instrumental conditions. A general form for the relationship between direct (blood-glucose concentration) and the indirect (optical) measurements is G=.function.(y.sub.1, y.sub.2, . . . ,y.sub.q), where G is the desired estimated value of the direct measurement (glucose), .function. is some function (model), and y.sub.1, y.sub.2, . . . ,y.sub.q (the arguments of .function.) represents the indirect (optical) measurement, or transformed optical measurements, at q wavelengths. The goal of this first step is to develop a useful function, .function.. In the second step, prediction, this function is evaluated at a measured set of indirect (optical) measurements {y.sub.1, y.sub.2, . . . ,y.sub.q } in order to obtain an estimate of the direct measurement (blood-glucose concentration) at some time in the future when optical measurements will be made without a corresponding direct or invasive measurement.
Ideally, one would prefer to develop a calibration model that is applicable across all subjects. Many such systems have been proposed as discussed above. However, it has been shown that for many applications the variability of the items being measured makes it difficult to develop such a universal calibration model. For the glucose application, the variability is across subjects with respect to the optical appearance of tissue and, possibly, across the analyte within the tissue.
FIG. 1 indicates the levels of spectral variation observed both among and within subjects during an experiment in which 84 measurements were obtained from each of 8 subjects. Sources of spectral variation within a subject include: spatial effects across the tissue, physiological changes within the tissue during the course of the experiment, sampling effects related to the interaction between the instrument and the tissue, and instrumental/environmental effects. The spectral variation across subjects is substantially larger than the sum of all effects within a subject. In this case the subjects were from a relatively homogeneous population. In the broader population it is expected that spectral variation across subjects will be substantially increased. Thus, the task of building a universal calibration model is a daunting one.
In order to avoid the issue of variability across subjects, one approach involves building a completely new model for each subject. Such a method involves a substantial period of observation for each subject, as taught by R. Marbach et al.,"Noninvasive Blood Glucose Assay by Near-Infrared Diffuse Reflectance Spectroscopy of the Human Inner Lip," Applied Spectroscopy, 1993, 47, 875-881. This method would be inefficient and impractical for commercial glucose applications due to the intensive optical sampling that would be needed for each subject.
Another approach taught by K. Ward et al., "Post-Prandial Blood Glucose Determination by Quantitative Mid-Infrared Spectroscopy," Applied Spectroscopy, 1992, 46, 959-965, utilizes partial least-squares multivariate calibration models based on whole blood glucose levels. When the models were based on in vitro measurements using whole blood, a subject-dependent concentration bias was retrospectively observed, indicating that additional calibration would be necessary.
In an article by Haaland et al., "Reagentless Near-Infrared Determination of Glucose in Whole Blood Using Multivariate Calibration," Applied Spectroscopy, 1992, 46, 1575-1578, the authors suggest the use of derivative spectra for reducing subject-to-subject (or inter-subject) spectral differences. The method was not found to be effective on the data presented in the paper. First derivatives are an example of a general set of processing methods that are commonly used for spectral pretreatrment. A general but incomplete list of these pretreatment methods would include trimming, wavelength selection, centering, scaling, normalization, taking first or higher derivatives, smoothing, Fourier transforming, principle component selection, linearization, and transformation. This general class of processing methods has been examined by the inventors and has not been found to effectively reduce the spectral variance to the level desired for clinical prediction results.
In an article by Lorber et al., "Local Centering in Multivariate Calibration," Journal of Chemometrics, 1996, 10, 215-220, a method of local centering the calibration data by using a single spectrum is described. For each unknown sample, the spectrum used for centering the calibration data set is selected to be that spectrum that is the closest match (with respect to Mahalanobis distance) to the spectrum. of the unknown. A separate partial least-squares model is then constructed for each unknown. The method does not reduce the overall spectroscopic variation in the calibration data set.
Accordingly, the need exists for a method and apparatus for non-invasively measuring attributes of biological tissue, such as glucose concentrations in blood, which incorporates a model that is sufficiently robust to act as an accurate surrogate for direct measurement. The model would preferably account for variability both between subjects and within the subject on which the indirect measurement is being used as a predictor. In order to be commercially successful, applicants believe, the model should not require extensive sampling of the specific subject on which the model is to be applied in order to accurately predict a biological attribute such as glucose. Extensive calibration of each subject is currently being proposed by BioControl Inc. In a recent press release the company defines a 60-day calibration procedure followed by a 30-day evaluation period.
The present invention addresses these needs as well as other problems associated with existing models and calibrations used in methods for non-invasively measuring an attribute of a biological sample such as glucose concentration in blood. The present invention also offers further advantages over the prior art and solves problems associated therewith.